NSF Award
2318814
Illicit drug abuse has become another major national health crisis since the Covid-19<br/>pandemic started, due to long period of quarantine at home with significantly reduced social<br/>interactions. In 2022, U.S. drug overdose deaths hit the highest level in history: nearly 110,000<br/>people died from drug overdose according to US Centers for Disease Control and Prevention. The<br/>top overdose drugs are opioids, cocaine, psychostimulants, and methadone. Mixing multiple drugs<br/>can also cause drug-drug interactions which may increase the risk of death. The current drug<br/>detection apparatuses typically require time-consuming, laborious sample preparation procedure<br/>and trained staff. These detection methods are not suitable for monitoring and profiling the current<br/>drug overdose crisis en masse. This project aims to develop a high throughput, label-free and<br/>portable sensor that can quantitatively detect multiple drugs (opioids, cocaine, psychostimulants,<br/>and methadone) in a liquid sample via a single test. The samples can be collected in the diverse<br/>forms of biofluids such as saliva, urine, sweat and blood. Successful development of this automatic,<br/>accurate, point-of-care platform will greatly simplify and accelerate the drug screen process.<br/><br/>The main module of the sensing platform consists of a silver (Ag) or gold (Au) nanoparticle<br/>decorated Zinc Oxide nanorod coated silica nanofiber matrix (Ag/AuNP-ZnONR-SNF<br/>nanosensor). Machine learning algorithm will be incorporated to achieve the automatic,<br/>quantitative analysis of multiplex detection of the drugs without trained expertise. The objective<br/>of this project will be achieved by accomplishing the following three research tasks: (1)<br/>Development and characterization of the nanosensor material to experimentally demonstrate the<br/>feasibility of surfaced enhanced plasmonic sensing of drugs using the device. The device is<br/>fabricated by electrospinning of the silica nanofiber as the supporting matrix, hydrothermal growth<br/>of the ZnO nanorod coated on the silica nanofiber, and Ag and Au nanoparticles synthesized by<br/>UV irradiation or seed mediated growth method, respectively, on the surface of the ZnONR-SNF<br/>matrix. (2) Optimization of the sensing performance, including the sensitivity, limit of detection<br/>(LoD), repeatability and stability of the sensor by tuning the geometries, dimensions, and structure<br/>of the nanomaterials-based sensing module with respect to different biofluidic samples. (3)<br/>Development of machine learning (ML) algorithms using prior-embedded deep neural network<br/>models trained by many data samples obtained using our sensor to identify and quantify multiple<br/>drugs from different sample sources. The successful implementation of the algorithm will allow<br/>for an accurate, automatic, quick, and multiplex detection of the drugs. This project will provide<br/>new methodologies and data to address the challenges in understanding and monitoring the current<br/>drug overdose crisis.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
